




'''simple docstring'''

from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


a       :						Optional[Any]								=		logging.get_logger(__name__)

a       :						Union[str, Any]								=		{
    '''google/umt5-small''': '''https://huggingface.co/google/umt5-small/resolve/main/config.json''',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}



class 		SCREAMING_SNAKE_CASE__  (			__A	):
			__SCREAMING_SNAKE_CASE    =			'''umt5'''
			__SCREAMING_SNAKE_CASE    =			['''past_key_values''']





			def __init__(      self						:    Optional[Any]      ,		a_						:    Union[str, Any]=250_112      ,		a_						:    List[str]=512      ,		a_						:    int=64      ,		a_						:    List[str]=1_024      ,		a_						:    int=8      ,		a_						:    List[Any]=None      ,		a_						:    Optional[Any]=6      ,		a_						:    Any=32      ,		a_						:    Any=128      ,		a_						:    str=0.1      ,		a_						:    int=1e-6      ,		a_						:    str=1.0      ,		a_						:    Tuple="gated-gelu"      ,		a_						:    Optional[int]=True      ,		a_						:    Any=True      ,		a_						:    Dict="T5Tokenizer"      ,		a_						:    str=True      ,		a_						:    Tuple=0      ,		a_						:    int=1      ,		a_						:    Any=0      ,		**a_						:    str      ,		):



						"""simple docstring"""




						super().__init__(
						    is_encoder_decoder=UpperCamelCase__      ,		tokenizer_class=UpperCamelCase__      ,		tie_word_embeddings=UpperCamelCase__      ,		pad_token_id=UpperCamelCase__      ,		eos_token_id=UpperCamelCase__      ,		decoder_start_token_id=UpperCamelCase__      ,		**UpperCamelCase__      ,		)
						__snake_case			=		vocab_size
						__snake_case			=		d_model
						__snake_case			=		d_kv
						__snake_case			=		d_ff
						__snake_case			=		num_layers
						__snake_case			=		(
						    num_decoder_layers if num_decoder_layers is not None else self.num_layers
						)  # default = symmetry
						__snake_case			=		num_heads
						__snake_case			=		relative_attention_num_buckets
						__snake_case			=		relative_attention_max_distance
						__snake_case			=		dropout_rate
						__snake_case			=		layer_norm_epsilon
						__snake_case			=		initializer_factor
						__snake_case			=		feed_forward_proj
						__snake_case			=		use_cache

						__snake_case			=		self.feed_forward_proj.split("-"							)
						__snake_case			=		act_info[-1]
						__snake_case			=		act_info[0] == "gated"

						if len(UpperCamelCase__							) > 1 and act_info[0] != "gated" or len(UpperCamelCase__							) > 2:
									raise ValueError(
									    f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
									    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
									    "\'gated-gelu\' or \'relu\'"							)

						if feed_forward_proj == "gated-gelu":
									__snake_case			=		"gelu_new"





			@property
			def 							A (      self						:    Tuple							):



						"""simple docstring"""




						return self.d_model





			@property
			def 							A (      self						:    Optional[Any]							):



						"""simple docstring"""




						return self.num_heads






			@property
			def 							A (      self						:    Any							):



						"""simple docstring"""




						return self.num_layers







class 		SCREAMING_SNAKE_CASE__  (			__A	):





			@property
			# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
			def 							A (      self						:    Dict							):



						"""simple docstring"""




						__snake_case			=		{
						    "input_ids": {0: "batch", 1: "encoder_sequence"},
						    "attention_mask": {0: "batch", 1: "encoder_sequence"},
						}
						if self.use_past:
									__snake_case			=		"past_encoder_sequence + sequence"
									__snake_case			=		{0: "batch"}
									__snake_case			=		{0: "batch", 1: "past_decoder_sequence + sequence"}
						else:
									__snake_case			=		{0: "batch", 1: "decoder_sequence"}
									__snake_case			=		{0: "batch", 1: "decoder_sequence"}

						if self.use_past:
									self.fill_with_past_key_values_(UpperCamelCase__      ,		direction="inputs"							)

						return common_inputs





			@property
			# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
			def 							A (      self						:    List[str]							):



						"""simple docstring"""




						return 13






			@property
			def 							A (      self						:    Any							):



						"""simple docstring"""




						return 5e-4













'''simple docstring'''







import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)
__lowerCAmelCase        =							logging.getLogger(__name__)


def       __UpperCamelCase					(      lowercase_       :  str   ):
       """simple docstring"""




       a_						     =			git.Repo(search_parent_directories=lowercase_   )
       a_						     =			{
           'repo_id': str(lowercase_   ),
           'repo_sha': str(repo.head.object.hexsha   ),
           'repo_branch': str(repo.active_branch   ),
       }

       with open(os.path.join(lowercase_   ,       'git_log.json'   )   ,       'w'   ) as f:
              json.dump(lowercase_   ,       lowercase_   ,       indent=4   )


def       __UpperCamelCase					(      lowercase_       :  List[Any]   ):
       """simple docstring"""




       if params.n_gpu <= 0:
              a_						     =			0
              a_						     =			-1
              a_						     =			True
              a_						     =			False
              return

       assert torch.cuda.is_available()

       logger.info('Initializing GPUs'   )
       if params.n_gpu > 1:
              assert params.local_rank != -1

              a_						     =			int(os.environ['WORLD_SIZE']   )
              a_						     =			int(os.environ['N_GPU_NODE']   )
              a_						     =			int(os.environ['RANK']   )

              # number of nodes / node ID
              a_						     =			params.world_size // params.n_gpu_per_node
              a_						     =			params.global_rank // params.n_gpu_per_node
              a_						     =			True

              assert params.n_nodes == int(os.environ['N_NODES']   )
              assert params.node_id == int(os.environ['NODE_RANK']   )

       # local job (single GPU)
       else:
              assert params.local_rank == -1

              a_						     =			1
              a_						     =			0
              a_						     =			0
              a_						     =			0
              a_						     =			1
              a_						     =			1
              a_						     =			False

       # sanity checks
       assert params.n_nodes >= 1
       assert 0 <= params.node_id < params.n_nodes
       assert 0 <= params.local_rank <= params.global_rank < params.world_size
       assert params.world_size == params.n_nodes * params.n_gpu_per_node

       # define whether this is the master process / if we are in multi-node distributed mode
       a_						     =			params.node_id == 0 and params.local_rank == 0
       a_						     =			params.n_nodes > 1

       # summary
       a_						     =			F'--- Global rank: {params.global_rank} - '
       logger.info(PREFIX + 'Number of nodes: %i' % params.n_nodes   )
       logger.info(PREFIX + 'Node ID        : %i' % params.node_id   )
       logger.info(PREFIX + 'Local rank     : %i' % params.local_rank   )
       logger.info(PREFIX + 'World size     : %i' % params.world_size   )
       logger.info(PREFIX + 'GPUs per node  : %i' % params.n_gpu_per_node   )
       logger.info(PREFIX + 'Master         : %s' % str(params.is_master   )   )
       logger.info(PREFIX + 'Multi-node     : %s' % str(params.multi_node   )   )
       logger.info(PREFIX + 'Multi-GPU      : %s' % str(params.multi_gpu   )   )
       logger.info(PREFIX + 'Hostname       : %s' % socket.gethostname()   )

       # set GPU device
       torch.cuda.set_device(params.local_rank   )

       # initialize multi-GPU
       if params.multi_gpu:
              logger.info('Initializing PyTorch distributed'   )
              torch.distributed.init_process_group(
                  init_method='env://'   ,       backend='nccl'   ,       )


def       __UpperCamelCase					(      lowercase_       :  Union[str, Any]   ):
       """simple docstring"""




       np.random.seed(args.seed   )
       torch.manual_seed(args.seed   )
       if args.n_gpu > 0:
              torch.cuda.manual_seed_all(args.seed   )




